Overview

Dataset statistics

Number of variables18
Number of observations35064
Missing cells5146
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 MiB
Average record size in memory144.0 B

Variable types

Numeric15
Categorical3

Alerts

station has constant value ""Constant
CO is highly overall correlated with NO2 and 3 other fieldsHigh correlation
DEWP is highly overall correlated with PRES and 1 other fieldsHigh correlation
NO2 is highly overall correlated with CO and 4 other fieldsHigh correlation
No is highly overall correlated with yearHigh correlation
O3 is highly overall correlated with NO2 and 1 other fieldsHigh correlation
PM10 is highly overall correlated with CO and 3 other fieldsHigh correlation
PM2.5 is highly overall correlated with CO and 2 other fieldsHigh correlation
PRES is highly overall correlated with DEWP and 1 other fieldsHigh correlation
SO2 is highly overall correlated with CO and 1 other fieldsHigh correlation
TEMP is highly overall correlated with DEWP and 2 other fieldsHigh correlation
WSPM is highly overall correlated with NO2High correlation
year is highly overall correlated with NoHigh correlation
PM2.5 has 696 (2.0%) missing valuesMissing
PM10 has 484 (1.4%) missing valuesMissing
SO2 has 669 (1.9%) missing valuesMissing
NO2 has 754 (2.2%) missing valuesMissing
CO has 1297 (3.7%) missing valuesMissing
O3 has 1078 (3.1%) missing valuesMissing
RAIN is highly skewed (γ1 = 26.53152903)Skewed
No is uniformly distributedUniform
No has unique valuesUnique
hour has 1461 (4.2%) zerosZeros
RAIN has 33684 (96.1%) zerosZeros
WSPM has 635 (1.8%) zerosZeros

Reproduction

Analysis started2024-03-08 05:17:18.731463
Analysis finished2024-03-08 05:18:04.007421
Duration45.28 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

No
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct35064
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17532.5
Minimum1
Maximum35064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:18:04.139145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1754.15
Q18766.75
median17532.5
Q326298.25
95-th percentile33310.85
Maximum35064
Range35063
Interquartile range (IQR)17531.5

Descriptive statistics

Standard deviation10122.249
Coefficient of variation (CV)0.57734204
Kurtosis-1.2
Mean17532.5
Median Absolute Deviation (MAD)8766
Skewness0
Sum6.1475958 × 108
Variance1.0245993 × 108
MonotonicityStrictly increasing
2024-03-08T12:18:04.354568image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
23379 1
 
< 0.1%
23373 1
 
< 0.1%
23374 1
 
< 0.1%
23375 1
 
< 0.1%
23376 1
 
< 0.1%
23377 1
 
< 0.1%
23378 1
 
< 0.1%
23380 1
 
< 0.1%
23422 1
 
< 0.1%
Other values (35054) 35054
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
35064 1
< 0.1%
35063 1
< 0.1%
35062 1
< 0.1%
35061 1
< 0.1%
35060 1
< 0.1%
35059 1
< 0.1%
35058 1
< 0.1%
35057 1
< 0.1%
35056 1
< 0.1%
35055 1
< 0.1%

year
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
2016
8784 
2014
8760 
2015
8760 
2013
7344 
2017
1416 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters140256
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Length

2024-03-08T12:18:04.642807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:18:04.803177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Most occurring characters

ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140256
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5229295
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:18:04.991759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4487524
Coefficient of variation (CV)0.52871219
Kurtosis-1.2080577
Mean6.5229295
Median Absolute Deviation (MAD)3
Skewness-0.0092942217
Sum228720
Variance11.893893
MonotonicityNot monotonic
2024-03-08T12:18:05.164078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 2976
8.5%
5 2976
8.5%
7 2976
8.5%
8 2976
8.5%
10 2976
8.5%
12 2976
8.5%
1 2976
8.5%
4 2880
8.2%
6 2880
8.2%
9 2880
8.2%
Other values (2) 5592
15.9%
ValueCountFrequency (%)
1 2976
8.5%
2 2712
7.7%
3 2976
8.5%
4 2880
8.2%
5 2976
8.5%
6 2880
8.2%
7 2976
8.5%
8 2976
8.5%
9 2880
8.2%
10 2976
8.5%
ValueCountFrequency (%)
12 2976
8.5%
11 2880
8.2%
10 2976
8.5%
9 2880
8.2%
8 2976
8.5%
7 2976
8.5%
6 2880
8.2%
5 2976
8.5%
4 2880
8.2%
3 2976
8.5%

day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.729637
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:18:05.345596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8002175
Coefficient of variation (CV)0.55946729
Kurtosis-1.1940295
Mean15.729637
Median Absolute Deviation (MAD)8
Skewness0.0067598056
Sum551544
Variance77.443829
MonotonicityNot monotonic
2024-03-08T12:18:05.850611image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1152
 
3.3%
2 1152
 
3.3%
28 1152
 
3.3%
27 1152
 
3.3%
26 1152
 
3.3%
25 1152
 
3.3%
24 1152
 
3.3%
23 1152
 
3.3%
22 1152
 
3.3%
21 1152
 
3.3%
Other values (21) 23544
67.1%
ValueCountFrequency (%)
1 1152
3.3%
2 1152
3.3%
3 1152
3.3%
4 1152
3.3%
5 1152
3.3%
6 1152
3.3%
7 1152
3.3%
8 1152
3.3%
9 1152
3.3%
10 1152
3.3%
ValueCountFrequency (%)
31 672
1.9%
30 1056
3.0%
29 1080
3.1%
28 1152
3.3%
27 1152
3.3%
26 1152
3.3%
25 1152
3.3%
24 1152
3.3%
23 1152
3.3%
22 1152
3.3%

hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros1461
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:18:06.073065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9222853
Coefficient of variation (CV)0.60193785
Kurtosis-1.2041745
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum403236
Variance47.918033
MonotonicityNot monotonic
2024-03-08T12:18:06.254205image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1461
 
4.2%
1 1461
 
4.2%
22 1461
 
4.2%
21 1461
 
4.2%
20 1461
 
4.2%
19 1461
 
4.2%
18 1461
 
4.2%
17 1461
 
4.2%
16 1461
 
4.2%
15 1461
 
4.2%
Other values (14) 20454
58.3%
ValueCountFrequency (%)
0 1461
4.2%
1 1461
4.2%
2 1461
4.2%
3 1461
4.2%
4 1461
4.2%
5 1461
4.2%
6 1461
4.2%
7 1461
4.2%
8 1461
4.2%
9 1461
4.2%
ValueCountFrequency (%)
23 1461
4.2%
22 1461
4.2%
21 1461
4.2%
20 1461
4.2%
19 1461
4.2%
18 1461
4.2%
17 1461
4.2%
16 1461
4.2%
15 1461
4.2%
14 1461
4.2%

PM2.5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct585
Distinct (%)1.7%
Missing696
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean85.024136
Minimum3
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:18:06.482140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q123
median60
Q3116
95-th percentile256
Maximum999
Range996
Interquartile range (IQR)93

Descriptive statistics

Standard deviation85.975981
Coefficient of variation (CV)1.011195
Kurtosis6.698026
Mean85.024136
Median Absolute Deviation (MAD)42
Skewness2.1018226
Sum2922109.5
Variance7391.8693
MonotonicityNot monotonic
2024-03-08T12:18:06.744424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 735
 
2.1%
10 553
 
1.6%
11 545
 
1.6%
9 540
 
1.5%
12 507
 
1.4%
8 506
 
1.4%
7 500
 
1.4%
13 458
 
1.3%
6 453
 
1.3%
14 442
 
1.3%
Other values (575) 29129
83.1%
(Missing) 696
 
2.0%
ValueCountFrequency (%)
3 735
2.1%
4 275
 
0.8%
5 337
1.0%
6 453
1.3%
7 500
1.4%
8 506
1.4%
8.8 1
 
< 0.1%
9 540
1.5%
10 553
1.6%
11 545
1.6%
ValueCountFrequency (%)
999 1
< 0.1%
857 1
< 0.1%
826 1
< 0.1%
823 1
< 0.1%
804 1
< 0.1%
748 1
< 0.1%
730 1
< 0.1%
704 1
< 0.1%
690 1
< 0.1%
687 2
< 0.1%

PM10
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct648
Distinct (%)1.9%
Missing484
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean112.22346
Minimum2
Maximum961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:18:07.053015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q139
median91
Q3154
95-th percentile300
Maximum961
Range959
Interquartile range (IQR)115

Descriptive statistics

Standard deviation97.59321
Coefficient of variation (CV)0.86963288
Kurtosis5.8530021
Mean112.22346
Median Absolute Deviation (MAD)56
Skewness1.8716685
Sum3880687.2
Variance9524.4346
MonotonicityNot monotonic
2024-03-08T12:18:07.280680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 473
 
1.3%
5 321
 
0.9%
14 299
 
0.9%
18 293
 
0.8%
22 288
 
0.8%
16 278
 
0.8%
24 278
 
0.8%
13 278
 
0.8%
17 267
 
0.8%
20 261
 
0.7%
Other values (638) 31544
90.0%
(Missing) 484
 
1.4%
ValueCountFrequency (%)
2 12
 
< 0.1%
3 93
 
0.3%
4 37
 
0.1%
5 321
0.9%
6 473
1.3%
7 181
 
0.5%
8 175
 
0.5%
9 225
0.6%
10 215
0.6%
11 242
0.7%
ValueCountFrequency (%)
961 1
< 0.1%
952 1
< 0.1%
950 1
< 0.1%
931 1
< 0.1%
929 1
< 0.1%
915 1
< 0.1%
902 1
< 0.1%
890 1
< 0.1%
886 1
< 0.1%
836 1
< 0.1%

SO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct279
Distinct (%)0.8%
Missing669
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean17.148603
Minimum0.2856
Maximum411
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:18:07.511096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2856
5-th percentile2
Q13
median8
Q321
95-th percentile67
Maximum411
Range410.7144
Interquartile range (IQR)18

Descriptive statistics

Standard deviation23.940834
Coefficient of variation (CV)1.3960807
Kurtosis13.150821
Mean17.148603
Median Absolute Deviation (MAD)6
Skewness2.9489241
Sum589826.2
Variance573.16352
MonotonicityNot monotonic
2024-03-08T12:18:07.728396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 8337
23.8%
3 2470
 
7.0%
4 1873
 
5.3%
5 1556
 
4.4%
6 1420
 
4.0%
8 1174
 
3.3%
7 1155
 
3.3%
9 995
 
2.8%
10 900
 
2.6%
11 774
 
2.2%
Other values (269) 13741
39.2%
ValueCountFrequency (%)
0.2856 2
 
< 0.1%
0.5712 2
 
< 0.1%
0.8568 8
 
< 0.1%
1 124
 
0.4%
1.1424 11
 
< 0.1%
1.428 15
 
< 0.1%
1.7136 7
 
< 0.1%
1.9992 13
 
< 0.1%
2 8337
23.8%
2.2848 10
 
< 0.1%
ValueCountFrequency (%)
411 1
< 0.1%
299 1
< 0.1%
273 1
< 0.1%
272 1
< 0.1%
256 1
< 0.1%
245 1
< 0.1%
236 1
< 0.1%
229 1
< 0.1%
219 1
< 0.1%
217 1
< 0.1%

NO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct422
Distinct (%)1.2%
Missing754
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean55.52956
Minimum2
Maximum251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:18:07.927715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile11
Q128
median49
Q377
95-th percentile124
Maximum251
Range249
Interquartile range (IQR)49

Descriptive statistics

Standard deviation35.80805
Coefficient of variation (CV)0.64484663
Kurtosis0.89190623
Mean55.52956
Median Absolute Deviation (MAD)24
Skewness0.97162044
Sum1905219.2
Variance1282.2164
MonotonicityNot monotonic
2024-03-08T12:18:08.161769image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 468
 
1.3%
30 464
 
1.3%
29 458
 
1.3%
28 456
 
1.3%
20 448
 
1.3%
26 446
 
1.3%
22 439
 
1.3%
38 438
 
1.2%
36 430
 
1.2%
35 428
 
1.2%
Other values (412) 29835
85.1%
(Missing) 754
 
2.2%
ValueCountFrequency (%)
2 57
 
0.2%
3 43
 
0.1%
4 63
 
0.2%
5 112
0.3%
5.9537 1
 
< 0.1%
6 174
0.5%
6.5696 1
 
< 0.1%
6.7749 1
 
< 0.1%
7 195
0.6%
7.1855 1
 
< 0.1%
ValueCountFrequency (%)
251 1
< 0.1%
241 1
< 0.1%
235 1
< 0.1%
229 1
< 0.1%
228 1
< 0.1%
224 1
< 0.1%
222 1
< 0.1%
221 2
< 0.1%
220.9028 1
< 0.1%
220 1
< 0.1%

CO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct116
Distinct (%)0.3%
Missing1297
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean1370.395
Minimum100
Maximum9800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:18:08.421392image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile300
Q1600
median1000
Q31700
95-th percentile3800
Maximum9800
Range9700
Interquartile range (IQR)1100

Descriptive statistics

Standard deviation1223.1391
Coefficient of variation (CV)0.89254491
Kurtosis7.9141578
Mean1370.395
Median Absolute Deviation (MAD)500
Skewness2.3949878
Sum46274129
Variance1496069.3
MonotonicityNot monotonic
2024-03-08T12:18:08.659218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 2341
 
6.7%
400 2227
 
6.4%
700 2157
 
6.2%
800 2122
 
6.1%
500 2104
 
6.0%
600 2100
 
6.0%
900 1820
 
5.2%
1000 1736
 
5.0%
1100 1571
 
4.5%
1200 1488
 
4.2%
Other values (106) 14101
40.2%
ValueCountFrequency (%)
100 243
 
0.7%
200 847
 
2.4%
300 2341
6.7%
400 2227
6.4%
500 2104
6.0%
600 2100
6.0%
700 2157
6.2%
800 2122
6.1%
900 1820
5.2%
1000 1736
5.0%
ValueCountFrequency (%)
9800 2
 
< 0.1%
9700 2
 
< 0.1%
9600 5
< 0.1%
9500 5
< 0.1%
9400 3
 
< 0.1%
9300 4
< 0.1%
9200 6
< 0.1%
9100 7
< 0.1%
9000 9
< 0.1%
8900 8
< 0.1%

O3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct716
Distinct (%)2.1%
Missing1078
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean56.229904
Minimum0.2142
Maximum358
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:18:08.873700image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2142
5-th percentile2
Q18
median42
Q382
95-th percentile180
Maximum358
Range357.7858
Interquartile range (IQR)74

Descriptive statistics

Standard deviation57.08271
Coefficient of variation (CV)1.0151664
Kurtosis1.8896642
Mean56.229904
Median Absolute Deviation (MAD)35
Skewness1.4006449
Sum1911029.5
Variance3258.4358
MonotonicityNot monotonic
2024-03-08T12:18:09.104956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3596
 
10.3%
1 1300
 
3.7%
3 708
 
2.0%
4 681
 
1.9%
5 608
 
1.7%
6 602
 
1.7%
7 548
 
1.6%
8 534
 
1.5%
9 388
 
1.1%
10 371
 
1.1%
Other values (706) 24650
70.3%
(Missing) 1078
 
3.1%
ValueCountFrequency (%)
0.2142 4
 
< 0.1%
0.4284 6
 
< 0.1%
0.6426 5
 
< 0.1%
0.8568 1
 
< 0.1%
1 1300
3.7%
1.071 6
 
< 0.1%
1.2852 7
 
< 0.1%
1.4994 4
 
< 0.1%
1.7136 3
 
< 0.1%
1.9278 4
 
< 0.1%
ValueCountFrequency (%)
358 1
< 0.1%
355 1
< 0.1%
347 1
< 0.1%
340 1
< 0.1%
338 2
< 0.1%
334 1
< 0.1%
333 1
< 0.1%
332 1
< 0.1%
331 1
< 0.1%
329 1
< 0.1%

TEMP
Real number (ℝ)

HIGH CORRELATION 

Distinct965
Distinct (%)2.8%
Missing19
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean13.784477
Minimum-16.8
Maximum40.6
Zeros226
Zeros (%)0.6%
Negative5072
Negative (%)14.5%
Memory size274.1 KiB
2024-03-08T12:18:09.331355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-16.8
5-th percentile-3.895
Q13.4
median14.8
Q323.5
95-th percentile30.7
Maximum40.6
Range57.4
Interquartile range (IQR)20.1

Descriptive statistics

Standard deviation11.385156
Coefficient of variation (CV)0.82594042
Kurtosis-1.1599163
Mean13.784477
Median Absolute Deviation (MAD)9.8
Skewness-0.10692388
Sum483076.98
Variance129.62179
MonotonicityNot monotonic
2024-03-08T12:18:09.553622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 291
 
0.8%
0 226
 
0.6%
1 218
 
0.6%
2 202
 
0.6%
-1 192
 
0.5%
24.1 149
 
0.4%
-2 144
 
0.4%
4 140
 
0.4%
24.6 139
 
0.4%
5 139
 
0.4%
Other values (955) 33205
94.7%
ValueCountFrequency (%)
-16.8 2
< 0.1%
-16.3 1
 
< 0.1%
-16.2 1
 
< 0.1%
-16.1 1
 
< 0.1%
-16 1
 
< 0.1%
-15.9 3
< 0.1%
-15.8 2
< 0.1%
-15.6 1
 
< 0.1%
-15.4 1
 
< 0.1%
-15.3 2
< 0.1%
ValueCountFrequency (%)
40.6 1
< 0.1%
39.8 1
< 0.1%
39 1
< 0.1%
38.8 1
< 0.1%
38.6 1
< 0.1%
38.4 1
< 0.1%
38.3 1
< 0.1%
38.1 1
< 0.1%
38 2
< 0.1%
37.8 2
< 0.1%

PRES
Real number (ℝ)

HIGH CORRELATION 

Distinct607
Distinct (%)1.7%
Missing19
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1011.5118
Minimum985.1
Maximum1042
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:18:09.748367image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum985.1
5-th percentile995.5
Q11002.8
median1011
Q31020
95-th percentile1028.9
Maximum1042
Range56.9
Interquartile range (IQR)17.2

Descriptive statistics

Standard deviation10.570928
Coefficient of variation (CV)0.010450623
Kurtosis-0.88188006
Mean1011.5118
Median Absolute Deviation (MAD)8.5
Skewness0.12509476
Sum35448431
Variance111.74452
MonotonicityNot monotonic
2024-03-08T12:18:09.936944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1024 271
 
0.8%
1025 249
 
0.7%
1023 238
 
0.7%
1018 237
 
0.7%
1026 235
 
0.7%
1022 231
 
0.7%
1027 219
 
0.6%
1017 216
 
0.6%
1021 214
 
0.6%
1016 208
 
0.6%
Other values (597) 32727
93.3%
ValueCountFrequency (%)
985.1 1
< 0.1%
985.4 1
< 0.1%
986.1 2
< 0.1%
986.7 1
< 0.1%
986.9 2
< 0.1%
987 2
< 0.1%
987.1 1
< 0.1%
987.2 2
< 0.1%
987.3 1
< 0.1%
987.5 2
< 0.1%
ValueCountFrequency (%)
1042 1
 
< 0.1%
1041.8 1
 
< 0.1%
1041.6 1
 
< 0.1%
1041.4 1
 
< 0.1%
1041.2 2
< 0.1%
1041.1 2
< 0.1%
1041 2
< 0.1%
1040.9 1
 
< 0.1%
1040.8 3
< 0.1%
1040.7 1
 
< 0.1%

DEWP
Real number (ℝ)

HIGH CORRELATION 

Distinct608
Distinct (%)1.7%
Missing19
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2.7074419
Minimum-35.3
Maximum28.5
Zeros70
Zeros (%)0.2%
Negative15264
Negative (%)43.5%
Memory size274.1 KiB
2024-03-08T12:18:10.157646image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-35.3
5-th percentile-19.8
Q1-8.5
median3.3
Q315.2
95-th percentile22.2
Maximum28.5
Range63.8
Interquartile range (IQR)23.7

Descriptive statistics

Standard deviation13.704139
Coefficient of variation (CV)5.0616559
Kurtosis-1.1028939
Mean2.7074419
Median Absolute Deviation (MAD)11.9
Skewness-0.20161265
Sum94882.3
Variance187.80343
MonotonicityNot monotonic
2024-03-08T12:18:10.399307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.3 145
 
0.4%
15.8 132
 
0.4%
17.1 132
 
0.4%
17.6 131
 
0.4%
16.2 127
 
0.4%
16.7 127
 
0.4%
16.8 126
 
0.4%
17 125
 
0.4%
16.9 125
 
0.4%
16.3 123
 
0.4%
Other values (598) 33752
96.3%
ValueCountFrequency (%)
-35.3 1
< 0.1%
-35.1 1
< 0.1%
-35 1
< 0.1%
-34.8 1
< 0.1%
-34.5 1
< 0.1%
-34.3 2
< 0.1%
-34.2 1
< 0.1%
-34.1 1
< 0.1%
-33.8 1
< 0.1%
-33.7 1
< 0.1%
ValueCountFrequency (%)
28.5 1
 
< 0.1%
28.3 1
 
< 0.1%
28 2
 
< 0.1%
27.6 1
 
< 0.1%
27.5 1
 
< 0.1%
27.4 6
< 0.1%
27.3 4
< 0.1%
27.2 4
< 0.1%
27.1 5
< 0.1%
27 4
< 0.1%

RAIN
Real number (ℝ)

SKEWED  ZEROS 

Distinct121
Distinct (%)0.3%
Missing19
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.06432016
Minimum0
Maximum46.4
Zeros33684
Zeros (%)96.1%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:18:10.596089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum46.4
Range46.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7969807
Coefficient of variation (CV)12.390838
Kurtosis952.32085
Mean0.06432016
Median Absolute Deviation (MAD)0
Skewness26.531529
Sum2254.1
Variance0.63517823
MonotonicityNot monotonic
2024-03-08T12:18:10.807637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33684
96.1%
0.1 290
 
0.8%
0.2 156
 
0.4%
0.3 119
 
0.3%
0.4 75
 
0.2%
0.5 72
 
0.2%
0.6 61
 
0.2%
0.7 53
 
0.2%
0.9 41
 
0.1%
0.8 40
 
0.1%
Other values (111) 454
 
1.3%
ValueCountFrequency (%)
0 33684
96.1%
0.1 290
 
0.8%
0.2 156
 
0.4%
0.3 119
 
0.3%
0.4 75
 
0.2%
0.5 72
 
0.2%
0.6 61
 
0.2%
0.7 53
 
0.2%
0.8 40
 
0.1%
0.9 41
 
0.1%
ValueCountFrequency (%)
46.4 1
< 0.1%
36.6 1
< 0.1%
33.7 1
< 0.1%
33.1 1
< 0.1%
29.3 1
< 0.1%
24.1 1
< 0.1%
23.8 1
< 0.1%
23.7 1
< 0.1%
22.4 1
< 0.1%
21.7 1
< 0.1%

wd
Categorical

Distinct16
Distinct (%)< 0.1%
Missing79
Missing (%)0.2%
Memory size274.1 KiB
NE
3568 
SW
3428 
ENE
3080 
E
2807 
WNW
2565 
Other values (11)
19537 

Length

Max length3
Median length2
Mean length2.2358725
Min length1

Characters and Unicode

Total characters78222
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWNW
2nd rowWNW
3rd rowWNW
4th rowNW
5th rowWNW

Common Values

ValueCountFrequency (%)
NE 3568
10.2%
SW 3428
9.8%
ENE 3080
 
8.8%
E 2807
 
8.0%
WNW 2565
 
7.3%
NW 2552
 
7.3%
WSW 2546
 
7.3%
W 2464
 
7.0%
SSW 2457
 
7.0%
ESE 1772
 
5.1%
Other values (6) 7746
22.1%

Length

2024-03-08T12:18:11.356024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ne 3568
10.2%
sw 3428
9.8%
ene 3080
 
8.8%
e 2807
 
8.0%
wnw 2565
 
7.3%
nw 2552
 
7.3%
wsw 2546
 
7.3%
w 2464
 
7.0%
ssw 2457
 
7.0%
ese 1772
 
5.1%
Other values (6) 7746
22.1%

Most occurring characters

ValueCountFrequency (%)
W 22310
28.5%
E 19956
25.5%
N 18443
23.6%
S 17513
22.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 78222
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 22310
28.5%
E 19956
25.5%
N 18443
23.6%
S 17513
22.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 78222
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 22310
28.5%
E 19956
25.5%
N 18443
23.6%
S 17513
22.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 22310
28.5%
E 19956
25.5%
N 18443
23.6%
S 17513
22.4%

WSPM
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct96
Distinct (%)0.3%
Missing13
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.745314
Minimum0
Maximum13.2
Zeros635
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:18:11.605541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q10.9
median1.4
Q32.3
95-th percentile4.2
Maximum13.2
Range13.2
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.2063548
Coefficient of variation (CV)0.69119641
Kurtosis3.2903588
Mean1.745314
Median Absolute Deviation (MAD)0.6
Skewness1.5306899
Sum61175
Variance1.4552918
MonotonicityNot monotonic
2024-03-08T12:18:11.859749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1 1833
 
5.2%
1.2 1831
 
5.2%
1 1778
 
5.1%
0.9 1749
 
5.0%
1.3 1689
 
4.8%
0.8 1657
 
4.7%
1.4 1559
 
4.4%
0.7 1451
 
4.1%
1.5 1418
 
4.0%
1.6 1298
 
3.7%
Other values (86) 18788
53.6%
ValueCountFrequency (%)
0 635
 
1.8%
0.1 301
 
0.9%
0.2 362
 
1.0%
0.3 341
 
1.0%
0.4 555
 
1.6%
0.5 886
2.5%
0.6 1187
3.4%
0.7 1451
4.1%
0.8 1657
4.7%
0.9 1749
5.0%
ValueCountFrequency (%)
13.2 1
 
< 0.1%
10.1 1
 
< 0.1%
10 1
 
< 0.1%
9.9 1
 
< 0.1%
9.5 1
 
< 0.1%
9.2 1
 
< 0.1%
8.9 1
 
< 0.1%
8.8 1
 
< 0.1%
8.7 1
 
< 0.1%
8.6 4
< 0.1%

station
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
Wanshouxigong
35064 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters455832
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWanshouxigong
2nd rowWanshouxigong
3rd rowWanshouxigong
4th rowWanshouxigong
5th rowWanshouxigong

Common Values

ValueCountFrequency (%)
Wanshouxigong 35064
100.0%

Length

2024-03-08T12:18:12.037667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:18:12.182240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
wanshouxigong 35064
100.0%

Most occurring characters

ValueCountFrequency (%)
n 70128
15.4%
o 70128
15.4%
g 70128
15.4%
W 35064
7.7%
a 35064
7.7%
s 35064
7.7%
h 35064
7.7%
u 35064
7.7%
x 35064
7.7%
i 35064
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 420768
92.3%
Uppercase Letter 35064
 
7.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 70128
16.7%
o 70128
16.7%
g 70128
16.7%
a 35064
8.3%
s 35064
8.3%
h 35064
8.3%
u 35064
8.3%
x 35064
8.3%
i 35064
8.3%
Uppercase Letter
ValueCountFrequency (%)
W 35064
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 455832
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 70128
15.4%
o 70128
15.4%
g 70128
15.4%
W 35064
7.7%
a 35064
7.7%
s 35064
7.7%
h 35064
7.7%
u 35064
7.7%
x 35064
7.7%
i 35064
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 455832
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 70128
15.4%
o 70128
15.4%
g 70128
15.4%
W 35064
7.7%
a 35064
7.7%
s 35064
7.7%
h 35064
7.7%
u 35064
7.7%
x 35064
7.7%
i 35064
7.7%

Interactions

2024-03-08T12:18:00.397100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:20.287069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:22.289630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:24.565438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:27.413038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:29.942776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:32.973759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:36.363262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:38.859618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:41.592406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:44.266312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:48.029270image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:51.380491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:54.345981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:57.719028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:18:00.585924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:20.437592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:22.418988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:24.790996image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:27.584359image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:30.147815image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:33.206905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:36.560251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:39.055836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:41.747433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:44.500928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:48.274289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:51.639313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:54.620950image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:57.904650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:18:00.777046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:20.553166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:22.544240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:24.974744image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:27.694757image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:30.348906image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:33.395680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:36.755047image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:39.256851image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:41.899027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:45.226644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:48.489966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:51.816310image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:54.868514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:58.122824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:18:01.016350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:20.691966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:22.669897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:25.142878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:27.915371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:30.544686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:33.627250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:36.899153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:39.488878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:42.066546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:45.412552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:48.729370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:52.029928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:55.045178image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:58.301900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:18:01.169137image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:20.815655image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:22.775543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:25.331290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:28.066411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:30.691552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:34.564161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:37.052653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:39.659911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:42.246272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:45.564924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:48.950585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:52.218933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:55.221734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:58.438134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:18:01.307564image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:20.933215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:22.896699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:25.497459image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:28.194471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:30.861585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:34.717544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:37.202626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:39.798063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:42.432348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:45.730078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:49.207065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:52.413274image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:55.378214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:58.628211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:18:01.460880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:21.057856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:23.222690image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:25.661321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:28.321996image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:31.074174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:34.914976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:37.368133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:39.942658image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:42.629440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:45.926258image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:49.483253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:52.687730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:55.540065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:58.816775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:18:01.609380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:21.192031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:23.332452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:25.832808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:28.476582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:31.279976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:35.048523image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:37.516830image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:40.095933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:42.788964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:46.131249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:49.680888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:52.852986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:55.736642image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:58.973123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:18:01.780918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:21.335442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:23.441183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:25.979916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:28.637621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:31.475835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:35.216579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:37.684449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:40.269611image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:42.978012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:46.304020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:49.880701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:52.982880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:55.911195image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:59.146015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:18:01.931541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:21.469089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:23.563864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:26.157560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:28.806253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:31.688497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:35.366110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:37.842144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:40.443411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:43.155387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:46.490540image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:50.131027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:53.143425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:56.100913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:59.330764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:18:02.096205image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:21.631091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:23.691158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:26.377800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:29.007864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:31.901630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:35.532976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:38.042258image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:40.603630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:43.295131image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:46.685174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:50.387956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:53.345217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:56.312063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:59.531427image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:18:02.260306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:21.795233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:23.844822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:26.579075image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:29.177449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:32.149744image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:35.702063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:38.207420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:40.831223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:43.494573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:46.882002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:50.569327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:53.523701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:56.934730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:59.742988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:18:02.399211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:21.905766image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:24.012140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:26.768585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:29.382555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:32.355097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:35.862660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:38.389914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:40.975649image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:43.705233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:47.153592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:50.735966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:53.671855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:57.116759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:59.954110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:18:02.557293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:22.033551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:24.176756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:26.974686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:29.574641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:32.573710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:36.059674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:38.575806image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:41.159908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:43.941504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:47.398631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:50.954509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:53.881063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:57.297238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:18:00.137202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:18:02.714008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:22.165997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:24.438854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:27.171142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:29.761709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:32.769756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:36.223005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:38.706064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:41.329381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:44.093271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:47.649530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:51.155777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:54.094054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:17:57.521629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:18:00.260892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-03-08T12:18:12.299145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
CODEWPNO2NoO3PM10PM2.5PRESRAINSO2TEMPWSPMdayhourmonthwdyear
CO1.0000.1250.768-0.077-0.4670.7460.8530.0630.0070.554-0.216-0.460-0.005-0.0360.0740.0960.079
DEWP0.1251.000-0.002-0.1070.2700.1310.214-0.7750.173-0.3820.815-0.2120.021-0.0130.2550.1130.154
NO20.768-0.0021.000-0.049-0.6640.6570.6800.137-0.0830.470-0.292-0.5440.021-0.0700.0720.1090.077
No-0.077-0.107-0.0491.000-0.028-0.077-0.0500.2590.004-0.260-0.1180.0940.0180.0010.0440.1430.862
O3-0.4670.270-0.664-0.0281.000-0.228-0.278-0.4290.001-0.2250.6020.458-0.0130.281-0.1460.1380.062
PM100.7460.1310.657-0.077-0.2281.0000.877-0.075-0.0840.501-0.055-0.3080.0280.065-0.0230.0820.066
PM2.50.8530.2140.680-0.050-0.2780.8771.000-0.070-0.0300.492-0.065-0.3740.0010.004-0.0010.0660.057
PRES0.063-0.7750.1370.259-0.429-0.075-0.0701.000-0.0790.274-0.8320.0170.009-0.0350.0150.0730.182
RAIN0.0070.173-0.0830.0040.001-0.084-0.030-0.0791.000-0.1610.037-0.001-0.010-0.0080.0420.0080.004
SO20.554-0.3820.470-0.260-0.2250.5010.4920.274-0.1611.000-0.395-0.0910.0180.036-0.2270.0390.093
TEMP-0.2160.815-0.292-0.1180.602-0.055-0.065-0.8320.037-0.3951.0000.1390.0160.1390.1220.1000.149
WSPM-0.460-0.212-0.5440.0940.458-0.308-0.3740.017-0.001-0.0910.1391.000-0.0050.163-0.1410.1480.062
day-0.0050.0210.0210.018-0.0130.0280.0010.009-0.0100.0180.016-0.0051.0000.0000.0100.0280.000
hour-0.036-0.013-0.0700.0010.2810.0650.004-0.035-0.0080.0360.1390.1630.0001.0000.0000.1170.000
month0.0740.2550.0720.044-0.146-0.023-0.0010.0150.042-0.2270.122-0.1410.0100.0001.0000.0820.249
wd0.0960.1130.1090.1430.1380.0820.0660.0730.0080.0390.1000.1480.0280.1170.0821.0000.170
year0.0790.1540.0770.8620.0620.0660.0570.1820.0040.0930.1490.0620.0000.0000.2490.1701.000

Missing values

2024-03-08T12:18:02.961689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-08T12:18:03.409974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-08T12:18:03.816125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
0120133109.09.06.017.0200.062.00.31021.9-19.00.0WNW2.0Wanshouxigong
12201331111.011.07.014.0200.066.0-0.11022.4-19.30.0WNW4.4Wanshouxigong
2320133128.08.0NaN16.0200.059.0-0.61022.6-19.70.0WNW4.7Wanshouxigong
3420133138.08.03.016.0NaNNaN-0.71023.5-20.90.0NW2.6Wanshouxigong
4520133148.08.03.0NaN300.036.0-0.91024.1-21.70.0WNW2.5Wanshouxigong
56201331510.010.04.08.0200.064.0-1.61024.7-21.10.0NE2.0Wanshouxigong
6720133168.08.06.013.0300.061.0-2.41025.4-20.30.0NE2.3Wanshouxigong
7820133178.08.08.020.0300.054.0-0.81026.7-19.90.0NNE2.0Wanshouxigong
8920133183.06.09.023.0300.050.00.41027.3-19.40.0NE2.7Wanshouxigong
91020133193.06.010.018.0300.056.01.51027.4-19.70.0ENE2.9Wanshouxigong
NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
35054350552017228143.06.0NaN5.0NaN82.014.61013.3-15.60.0N3.6Wanshouxigong
350553505620172281511.021.02.05.0200.0NaN15.41013.0-15.00.0NNW3.3Wanshouxigong
35056350572017228166.020.03.0NaN200.080.014.91012.6-15.40.0NW2.1Wanshouxigong
350573505820172281711.023.03.012.0300.087.014.21012.5-14.90.0NW3.1Wanshouxigong
350583505920172281811.030.02.016.0300.082.013.41013.0-15.50.0WNW1.4Wanshouxigong
350593506020172281911.032.03.024.0400.072.012.51013.5-16.20.0NW2.4Wanshouxigong
350603506120172282013.032.03.041.0500.050.011.61013.6-15.10.0WNW0.9Wanshouxigong
350613506220172282114.028.04.038.0500.054.010.81014.2-13.30.0NW1.1Wanshouxigong
350623506320172282212.023.04.030.0400.059.010.51014.4-12.90.0NNW1.2Wanshouxigong
350633506420172282313.019.04.038.0600.049.08.61014.1-15.90.0NNE1.3Wanshouxigong